Abstract
With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data.
Highlights
In the context of urbanization and big data, rapid development of geospatial data acquisition has caused an explosive growth of geospatial datasets
With the popularity of volunteered geographic information (VGI), geospatial data updating is undergoing a significant transformation from top-down active updating to bottom-up crowd updating [1]
Detect candidate-matching building objects based on buffering analysis, aggregate neighboring objects into pattern combinations, and calculate the geo-similarities between candidate-matching objects and pattern combinations to initialize the matching matrix; Compute the contextual compatibilities between neighboring matching pairs to iteratively update the initial matching matrix, select the matching pairs based on the convergent matching matrix, and refine them through a matching conflict detection
Summary
In the context of urbanization and big data, rapid development of geospatial data acquisition has caused an explosive growth of geospatial datasets. Geo-Inf. 2019, 8, 38 identify the associated point, linear, or area objects between two or more geospatial datasets in the same or overlapping region, which is widely regarded as the essential step of data conflation and problems of information isolation. It is a is crucial issue in the GIS community to conflate multi-source map updating. Data matching aims semantic to identify the associated point, uneven scale large information geometric discrepancies, and diverse descriptions) possibly linear, or area objects between and two or more geospatial datasets the Researchers same or overlapping region, occur between crowdsourced authoritative geospatial datain[8].
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